Relevant bibliographies by topics / Neural time series / Dissertations / Theses
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Dissertations / Theses on the topic 'Neural time series'

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Published: 18 May 2024

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1

Kajitani, Yoshio. "Forecasting time series with neural nets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ39836.pdf.

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2

Andreux, Mathieu. "Foveal autoregressive neural time-series modeling." Electronic Thesis or Diss., Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE073.

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Cette thèse s'intéresse à la modélisation non-supervisée de séries temporelles univariées. Nous abordons tout d'abord le problème de prédiction linéaire des valeurs futures séries temporelles gaussiennes sous hypothèse de longues dépendances, qui nécessitent de tenir compte d'un large passé. Nous introduisons une famille d'ondelettes fovéales et causales qui projettent les valeurs passées sur un sous-espace adapté au problème, réduisant ainsi la variance des estimateurs associés. Dans un deuxième temps, nous cherchons sous quelles conditions les prédicteurs non-linéaires sont plus performants
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Bonato, Tommaso. "Time Series Predictions With Recurrent Neural Networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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L'obiettivo principale di questa tesi è studiare come gli algoritmi di apprendimento automatico (machine learning in inglese) e in particolare le reti neurali LSTM (Long Short Term Memory) possano essere utilizzati per prevedere i valori futuri di una serie storica regolare come, per esempio, le funzioni seno e coseno. Una serie storica è definita come una sequenza di osservazioni s_t ordinate nel tempo. Inoltre cercheremo di applicare gli stessi principi per prevedere i valori di una serie storica prodotta utilizzando i dati di vendita di un prodotto cosmetico durante un periodo di tre anni.
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Brax, Christoffer. "Recurrent neural networks for time-series prediction." Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-480.

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<p>Recurrent neural networks have been used for time-series prediction with good results. In this dissertation recurrent neural networks are compared with time-delayed feed forward networks, feed forward networks and linear regression models on a prediction task. The data used in all experiments is real-world sales data containing two kinds of segments: campaign segments and non-campaign segments. The task is to make predictions of sales under campaigns. It is evaluated if more accurate predictions can be made when only using the campaign segments of the data.</p><p>Throughout the entire proje
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ABELEM, ANTONIO JORGE GOMES. "ARTIFICIAL NEURAL NETWORKS IN TIME SERIES FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1994. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8489@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Esta dissertação investiga a utilização de Redes Neurais Artificiais (RNAs) na previsão de séries temporais, em particular de séries financeiras, consideradas uma classe especial de séries temporais, caracteristicamente ruídos e sem periodicidade aparente. O trabalho envolve quatro partes principais: um estudo sobre redes neurais artificiais e séries temporais; a modelagem das RNAs para previsão de séries temporais; o desenvolvimento de um ambiente de simulação; e o estudo de caso. No estudo sobre Redes Neurais Artificiais
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ZANDONADE, ELIANA. "USING NEURAL NETWORK IN TIME SERIES FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1993. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8641@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Este trabalho associa previsão de Séries Temporais a uma nova metodologia de processamento de informação: REDE NEURAL. Usaremos o modelo de Retropropagação, que consiste em uma Rede Neural multicamada com as unidades conectadas apenas com a unidades conectadas apenas com as unidades da camada subseqüente e com a informação passando em uma única direção. Aplicaremos o modelo de retropropagação na análise de quatro séries temporais: uma série ruidosa. Uma série com tendência, uma série sazonal e uma série de Consumo de Ene
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7

MELLEM, MARCELO TOURASSE NASSIM. "AUTOREGRESSIVE-NEURAL HYBRID MODELS FOR TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1997. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14541@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Este trabalho apresenta um modelo linear por partes chamado de modelo ARN. Trata-se de uma estrutura híbrida que envolve modelos autoregressivos e redes neurais. Este modelo é comparado com o modelo AR de coeficientes fixos e com a rede neural estática aplicada à previsão. Os resultados mostram que o ARN consegue identificar a estrutura não-linear dos dados simulados e que na maioria dos casos ele possui melhor habilidade preditiva do que os modelos supracitados.<br>In this thesis we develop a piece-wise linear model named ARN mod
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8

Han, Ying. "Analysing time series using artificial neural networks." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398318.

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9

Rana, Md Mashud. "Energy time series prediction." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11745.

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Reliable operations and economical utilization of power systems require electricity load forecasting at a wide range of forecasting horizons. The objective of this thesis is two-fold: developing accurate prediction models for electricity load forecasting, and quantifying the load forecasting uncertainty. At first, we consider the task of feature selection for electricity load forecasting. We propose a two-step approach - identifying a set of candidate features based on the data characteristics and then selecting a subset of them using four different methods. We evaluate the performance of th
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SOTO, CLAVER PARI. "TEMPORAL NEURAL NETWORKS FOR TREATING TIME VARIANT SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7437@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>As RNA Temporais, em função de sua estrutura, consideram o tempo na sua operação, incorporando memória de curto prazo distribuída na rede em todos os neurônios escondidos e em alguns dos casos nos neurônios de saída. Esta classe de redes é utilizada para representar melhor a natureza temporal dos sistemas dinâmicos. Em contraste, a RNA estática tem uma estrutura apropriada para tarefas de reconhecimento de padrões, classificação e outras de natureza estática ou estacionária tendo sido utilizada com sucesso em diversas apl
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Lombardi, Alessandro. "Multiple time series forecasting with Graph Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24729/.

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Time series forecasting aims to predict future values to support organizations making strategic decisions. This problem has been studied for decades due to its relevance in almost all industries and areas, ranging from financial data to product demand. Recently, modern solutions based on deep learning have gained popularity among academia and industry, mainly due to the necessity to automatize the forecasting of multiple time series and exploit external explanatory variables. Considering the recent successes of Graph Neural Networks (GNNs) in modelling graph data, this study extends previous w
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Liotta, Andrea <1987&gt. "evolutionary wavelet neural networks for time series forecasting." Master's Degree Thesis, Università Ca' Foscari Venezia, 2013. http://hdl.handle.net/10579/3447.

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Dodd, Tony. "Prior knowledge for time series modelling." Thesis, University of Southampton, 2000. https://eprints.soton.ac.uk/254110/.

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Borra, Davide <1992&gt. "Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10345/1/phdthesis_dborra.pdf.

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Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and h
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Sarishvili, Alex. "Neural network based lag selection for multivariate time series." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=966609611.

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Andoh, Charles. "Risk analysis of financial time series using neural networks." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974193461.

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Aamodt, Rune. "Using Artificial Neural Networks To Forecast Financial Time Series." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10907.

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This thesis investigates the application of artificial neural networks (ANNs) for forecasting financial time series (e.g. stock prices).The theory of technical analysis dictates that there are repeating patterns that occur in the historic prices of stocks, and that identifying these patterns can be of help in forecasting future price developments. A system was therefore developed which contains several ``agents'', each producing recommendations on the stock price based on some aspect of technical analysis theory. It was then tested if ANNs, using these recommendations as inputs, could be train
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Chan, Lipton. "Time-series prediction using evolutionary lateral-delay neural networks." Thesis, University of Glasgow, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272850.

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Ghazali, Rozaida. "Higher order neural networks for financial time series prediction." Thesis, Liverpool John Moores University, 2007. http://researchonline.ljmu.ac.uk/5879/.

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Neural networks have been shown to be a promising tool for forecasting financial times series. Numerous research and applications of neural networks in business have proven their advantage in relation to classical methods that do not include artificial intelligence. What makes this particular use of neural networks so attractive to financial analysts and traders is the fact that governments and companies benefit from it to make decisions on investment and trading. However, when the number of inputs to the model and the number of training examples becomes extremely large, the training procedure
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Pan, Lingxue. "Resampling in neural networks with application to financial time series." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ47406.pdf.

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Burton, Holly. "Reservoir inflow forecasting using time series and neural network models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0017/MQ54220.pdf.

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Burton, Holly. "Reservoir inflow forecasting using time series and neural network models." Thesis, McGill University, 1998. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29800.

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In practice, the reservoir net inflow is computed based upon the application of the water balance equation to the reservoir system since it is difficult to obtain direct and reliable measurements of this variable. The net inflow process has been thus found to possess a random behaviour because it is related to the stochastic nature of various physical processes involved in the water balance computation (e.g., precipitation, evaporation, etc.). Therefore, the aim of this research is to propose a forecasting method that can accurately and efficiently predict the random reservoir inflow series. T
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MACHADO, MARIA AUGUSTA SOARES. "IDENTIFICATION OF NON-SEASONAL TIME SERIES THROUGH FUZZY NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2000. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7554@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Observando a dificuldade de batimento (match) dos padrões de comportamento das funções de autocorrelação e de autocorrelação parcial teóricas com as respectivas funções e as autocorrelação e de autocorrelação parcial estimadas de uma séries temporal, aliada ao fato da dificuldade em definir um número em específico como delimitador inequívoco do que seja um lag significativo, tornam clara a dose de julgamento subjetivo a ser realizado por um especialista de análise de séries temporais na tomada de decisão sobre a estrutur
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Aupke, Phil. "Uncertainity in Renewable Energy Time Series Prediction using Neural Networks." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-82714.

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With the increasing demand for solar energy, the forecast of the PV station energy production has to be as precisely as possible. To make the prediction more robust, also correlated infor- mation about the weather can be added to the previous energy production of the PV station. This thesis is part of a project, which has the goal to build an energy marketplace for a smart energy grid between households. To make the decisions of the prosumer more accurate, a forecast for the PV station energy production has to be as accurate as possible. Because not every household or even some smart grids wil
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Haddad, Josef, and Carl Piehl. "Unsupervised anomaly detection in time series with recurrent neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259655.

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Artificial neural networks (ANN) have been successfully applied to a wide range of problems. However, most of the ANN-based models do not attempt to model the brain in detail, but there are still some models that do. An example of a biologically constrained ANN is Hierarchical Temporal Memory (HTM). This study applies HTM and Long Short-Term Memory (LSTM) to anomaly detection problems in time series in order to compare their performance for this task. The shape of the anomalies are restricted to point anomalies and the time series are univariate. Pre-existing implementations that utilise these
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Kourentzes, Nikolaos. "Input variable selection for time series forecasting with artificial neural networks : an empirical evaluation across varying time series frequencies." Thesis, Lancaster University, 2009. http://eprints.lancs.ac.uk/60234/.

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Over the last two decades there has been an increase in the research of artificial neural networks (ANNs) to forecasting problems. Both in theoretical and empirical works, ANNs have shown evidence of good performance, in many cases outperforming established statistical benchmarks. This thesis starts by reviewing the advances in ANNs for time series forecasting, assessing their performance in the literature, analysing the current state of the art, the modelling issues that have been solved and which are still critical for forecasting with ANNs, thereby indicating future research directions. The
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Howells, Timothy Paul. "Pattern recognition in physiological time-series data using Bayesian neural networks." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/24717.

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This thesis describes the application of Bayesian techniques to the analysis of a large database of physiological time series data collected during the management of patients following traumatic brain injury at the Western General Hospital in Edinburgh. The study can be divided into three main sections: •   <i>Model validation using simulated data:</i> Techniques are developed that show that under certain conditions the distribution of network outputs generated by these Bayesian neural networks correctly models the desired conditional probability density functions for a wide range of simple pr
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Sandström, Carl. "An evolutionary approach to time series forecasting with artificial neural networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168224.

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In this paper an evolutionary approach to forecasting the stock market is tested and compared with backpropagation. An neuroevolutionary algorithm is implemented and backtested measuring returns and the normalized-mean-square-error for each algorithm on selected stocks from NASDAQ. The results are not entirely conclusive and further investigation would be needed to say definitely, but it seems as a neuroevolutionary approach could outperform backpropagation for time series prediction.
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Al-Hitmi, Mohammed Abdulla E. "Non-linear data analysis and neural networks for time series prediction." Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370084.

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FIALHO, MARCELLO MOREIRA STUCKERT. "APPLICATION OF INTERVAL NEURAL NETWORKS TO TIME SERIES FORECASTING AND TRADING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1996. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9297@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Esta dissertação apresenta uma proposta de arquitetura de redes neurais de intervalos para previsão de séries financeiras. O desempenho desta arquitetura é analisado através de testes de previsão para algumas séries de mercado. Como contribuição adicional é apresentado um algoritmo de trading automático. Este algoritmo é avaliado aplicando-o à séries de mercado, para mensuração de lucros percentuais. Por fim, dados de previsão, obtidos pela rede proposta, são utilizadas para a otimização do trading.<br>This text presents
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Taskaya-Temizel, Tugba. "Configuration of neural networks to model seasonal and cyclic time series." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/844482/.

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Time series often exhibit periodical patterns that can be analysed by conventional statistical techniques. These techniques rely upon an appropriate choice of model parameters that are often difficult to determine. Whilst neural networks also require an appropriate parameter configuration, they offer a way in which non-linear patterns may be modelled. However, evidence from a limited number of experiments has been used to argue that periodical patterns cannot be modelled using such networks. Researchers have argued that combining models for forecasting gives better estimates than single time s
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Börjesson, Lukas. "Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167331.

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In this paper, predictions of future price movements of a major American stock index was made by analysing past movements of the same and other correlated indices. A model that has shown very good results in speech recognition was modified to suit the analysis of financial data and was then compared to a base model, restricted by assumptions made for an efficient market. The performance of any model, that is trained by looking at past observations, is heavily influenced by how the division of the data into train, validation and test sets is made. This is further exaggerated by the temporal str
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Moradi, Mahdi. "TIME SERIES FORECASTING USING DUAL-STAGE ATTENTION-BASED RECURRENT NEURAL NETWORK." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2701.

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AN ABSTRACT OF THE RESEARCH PAPER OFMahdi Moradi, for the Master of Science degree in Computer Science, presented on April 1, 2020, at Southern Illinois University Carbondale.TITLE: TIME SERIES FORECASTING USING DUAL-STAGE ATTENTION-BASED RECURRENT NEURAL NETWORKMAJOR PROFESSOR: Dr. Banafsheh Rekabdar
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Hartvigsen, Thomas. "Adaptively-Halting RNN for Tunable Early Classification of Time Series." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1257.

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Early time series classification is the task of predicting the class label of a time series before it is observed in its entirety. In time-sensitive domains where information is collected over time it is worth sacrificing some classification accuracy in favor of earlier predictions, ideally early enough for actions to be taken. However, since accuracy and earliness are contradictory objectives, a solution to this problem must find a task-dependent trade-off. There are two common state-of-the-art methods. The first involves an analyst selecting a timestep at which all predictions must be made.
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Wasnik, Sachinkumar. "Fatigue Detection in EEG Time Series Data Using Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24917.

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Fatigue has widespread effects on the brain’s executive function, reaction time and information processing, causing loss of alertness, that affect safety, and productivity. There are various subjective and behavioural methods to measure fatigue. However, none of them is precise. The work in this thesis employs physiological measures such as heart rate, blood pressure, and breathing that are objective and quantitative indicators. These are thought to provide reliable measures of fatigue and may be easier to deploy in real world scenarios, compared to the subjective or behavioural methods. In p
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Zhai, Yusheng. "Time series forecasting competition among three sophisticated paradigms /." Electronic version (Microsoft Word), 2005. http://dl.uncw.edu/etd/2005/zhaiy/yushengzhai.html.

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Setyawati, Bina R. "Multi-layer feed forward neural networks for foreign exchange time series forecasting." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4180.

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Thesis (Ph. D.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains xii, 185 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 140-146).
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Gallant, Peter Joseph. "A hybrid evolutionary algorithm to train neural networks as time-series predictors." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ59526.pdf.

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Novak, Martina. "A neural network approach for simulation and forecasting of chaotic time series." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/19087.

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MEDEIROS, MARCELO CUNHA. "A LINEAR-NEURAL HYBRID MODEL FOR ANALYSIS AND FORECASTING OF TIME-SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1998. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14540@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Esta dissertação apresenta um modelo não linear auto-regressivo com variáveis exógenas (ARX), para análise e previsão de séries temporais. Os coeficientes do modelo são estimados pela saída de uma rede neural feed-forward, treinada por um algoritmo híbrido de otimização. Os resultados obtidos são comparados tanto com modelos lineares, quanto com não lineares.<br>This thesis presents a non linear autoregressive model with exogeneous variables (ARX), for time series analysis and forecasting. The coefficients of the model are given
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Chen, Tiankai M. Eng Massachusetts Institute of Technology. "Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120245.

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Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 92-94).<br>Semiconductor manufacturing provides unique challenges to the anomaly detection problem. With multiple recipes and multivariate data, it is difficult for engineers to reliably detect anomalies in the manufacturing process. An experimental study into anomaly detection through time series forecasting is carried out with application to a plasma etch case study. The
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McDonald, Scott. "Applications of self-organising fuzzy neural networks in financial time series analysis." Thesis, Ulster University, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.694650.

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The forecasting of financial time series is a major research area in statistics, econometrics and, increasingly, computational intelligence. Financial data are known to be extremely complex, nonstationary, and nonlinear in their composition. Machine learning algorithms have shown themselves to be capable of modelling complex datasets, particularly when compared with traditional statistical models. In particular, artificial neural networks are one of the most popular models in the literature. This thesis explores the usage of a particular type of neural network, namely a self organising fuzzy n
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Winn, David. "An analysis of neural networks and time series techniques for demand forecasting." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1004362.

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This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasti
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Tse, Peter W. "Neural networks for machine fault diagnosis and life span prediction." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390518.

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Vendramin, Nicoló. "Unsupervised Anomaly Detection on Multi-Process Event Time Series." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254885.

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Establishing whether the observed data are anomalous or not is an important task that has been widely investigated in literature, and it becomes an even more complex problem if combined with high dimensional representations and multiple sources independently generating the patterns to be analyzed. The work presented in this master thesis employs a data-driven pipeline for the definition of a recurrent auto-encoder architecture to analyze, in an unsupervised fashion, high-dimensional event time-series generated by multiple and variable processes interacting with a system. Facing the above menti
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46

Kasderidis, Stathis P. "A compartmental model neuron, its networks and application to time series." Thesis, King's College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313657.

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Capanni, Niccolo Francesco. "The functionality of spatial and time domain artificial neural models." Thesis, Robert Gordon University, 2006. http://hdl.handle.net/10059/241.

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This thesis investigates the functionality of the units used in connectionist Artificial Intelligence systems. Artificial Neural Networks form the foundation of the research and their units, Artificial Neurons, are first compared with alternative models. This initial work is mainly in the spatial-domain and introduces a new neural model, termed a Taylor Series neuron. This is designed to be flexible enough to assume most mathematical functions. The unit is based on Power Series theory and a specifically implemented Taylor Series neuron is demonstrated. These neurons are of particular usefulnes
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Tadjuidje, Kamgaing Joseph. "Competing neural networks as models for non stationary financial time series changepoint analysis /." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974108014.

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Silver-Warner, Stephen John. "Associative memory neural networks : an investigation with application to chaotic time series prediction." Thesis, Brunel University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362486.

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Kingdon, Jason Conrad. "Feed forward neural networks and genetic algorithms for automated financial time series modelling." Thesis, University College London (University of London), 1995. http://discovery.ucl.ac.uk/1318052/.

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This thesis presents an automated system for financial time series modelling. Formal and applied methods are investigated for combining feed-forward Neural Networks and Genetic Algorithms (GAs) into a single adaptive/learning system for automated time series forecasting. Four important research contributions arise from this investigation: i) novel forms of GAs are introduced which are designed to counter the representational bias associated with the conventional Holland GA, ii) an experimental methodology for validating neural network architecture design strategies is introduced, iii) a new me
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